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 Ocean Circulation And Air Sea Interaction: People
Zhijin  Li's Picture
Jet Propulsion Laboratory
M/S 300-323
4800 Oak Grove Drive
Pasadena, CA 91109

Zhijin Li

Research Interests
  • Conceptual development and mathematical formulation of atmospheric and oceanic data assimilation algorithms
  • Implementation of advanced data assimilation algorithms for real-time forecasting systems
  • Parallel computing of atmospheric and oceanic modeling and adjoint systems
  • Development and application of adjoint models associated with sophisticated atmospheric and oceanic models and their physics
  • Predictability


OurOcean Icon OurOcean
The JPL OurOcean Portal provides both real-time and retrospective analysis of remote sensing data and ocean model simulations in the Pacific Ocean.

OSTM/Jason 2 Icon OSTM/Jason 2
OSTM/Jason-2 provides continues the legacies of TOPEX/Poseidon and Jason-1 missions for measuring ocean surface dynamic topography.

SPURS (Salinity Processes in the Upper Ocean Regional Study) Icon SPURS (Salinity Processes in the Upper Ocean Regional Study)
Salinity Processes in the Upper Ocean Regional Studies (SPURS) are NASA Physical Oceanography field campaigns (including SPURS1 in the Atlantic Ocean and SPURS2 in the Pacific Ocean) with international participations.

The Surface Water Ocean Topography Mission provides measurements of land surface water storage and ocean surface dynamic topography.

Professional Experience
  • Jet Propulsion Laboratory - Research Technologist (2006-present)
  • Jet Propulsion Laboratory and Raytheon - Senior Physics Engineer (2001-2006)
  • Florida State University (1996-2001)
    • Research Associate, School of Computational Science Information Technology (2000-2001)
    • Postdoctoral Research Associate, Supercomputer Computations Research Institute (1996-2000)
  • Chinese Academy of Sciences, Beijing, China - Associate Professor, Institute of Atmospheric Physics (1994-1996)

Selected Publications
(More Than 70 Published)
  1. Li., Z., J. Wang, and L. Fu., 2019: An Observing System Simulation Experiment for ocean state estimation to assess the performance of the SWOT Mission. Part 1: A twin experiment, J. Geophy. Res., 124, 4838-4855.
  2. Li Z., F. M. Bingham, and P. Y. Li, 2019: Multiscale simulation, data assimilation and forecasting in support of the SPURS-2 field campaign. Oceanography, 32, 2, 76-83.
  3. Li. Z., C. Zuffada, S. T. Lowe, T. Lee and V. Zlotnicki, 2016: Analysis of GNSS-R Altimetry for Mapping Ocean Mesoscale Sea Surface Heights Using High-Resolution Model Simulations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 9, doi10.1109/JSTARS.2016.2581699.
  4. Feng, S., T. Lauvaux, S. Newman, P. Rao, R. Patarasuk, R. Ahmadov, A. Deng, K.W. Wong, D. O’Keeffe, J. Huang, Y. Song, K. Gurney, L.I. Diaz-Isaac, S. Jeong, M.L. Fischer, C.E. Miller, R.M. Duren, Z. Li, Y.L. Yung, S.P. Sander, 2016: Network Assessment for Atmospheric Monitoring of Urban CO2 Emissions Using a High-Resolution Land-Atmosphere Modelling System, Atmos. Chems. Phy., doi:10.5194/acp-2016-143.
  5. Li, Z., X. Chen, W. I. Gustafson, and A. Vogelmann, 2016: Spectral Characteristics of Background Error Covariance and Multiscale Data Assimilation, Int. J. Numer. Meth. Fluids, doi:10.1002/fld.4253
  6. Li, Z., J.C. McWilliams, K. Ide, and J.D. Fararra, 2015: A Multi-Scale Data Assimilation Scheme: Formulation and Illustration.  Mon. Wea. Rev., 143, 3804-3822.
  7. Li, Z., J.C. McWilliams, K. Ide, and J.D. Fararra, 2015: Coastal Ocean Data Assimilation Using A Multi-Scale Three-Dimensional Variational Scheme. Ocean Dynamics, 65, 1001-1015.
  8. Li, Z., S. Feng, Y. Liu, W. Lin, M. Zhang, T. Toto, A. Vogelmann, and S. Endo, 2014: Development of Fine Resolution Analysis and Expanded Properties of Large-Scale Forcing. Part I: Methodology and Evaluations, J. Geophy. Res., doi:10.1002/2014JD022245.
  9. Feng, S., Z. Li, Y. Liu, W. Lin, M. Zhang, A. Vogelmann,T. Toto, and, S. Endo, 2014: Developmen of Fine Resolution Analysis and Expanded Properties of Large-Scale Forcing. Part II, Scale-Awareness and Application to Single Column Models, J. Geophy. Res., doi: 10.1002/2014JD022254.
  10. Bingham, F. M., P. P. Li, Z. Li, Q. Vu, and Y. Chao, 2014: Data Management Support for the SPURS Atlantic Field Campaign, Oceanography, 28, 42-51.
  11. Bingham, F. M., J. Busecke, A. L. Gordon, C. F. Giulivi, Z. Li, 2014: The North Atlantic Subtropical Surface Salinity Maximum as Observed by Aquarius. J. Geophy. Res., doi:10.1022/2014JC009825.
  12. Busecke, J., A. L. Gordon, Z. Li, F. M. Bingham, and J. Font, 2014: Subtropical surface layer salinity budget and the role of mesoscale turbulence, J. Geophys. Res. Oceans, 119, 4124–4140, doi:10.1002/2013JC009715.
  13. Colas, F, X. Capet, J.C. McWilliams, Z. Li, 2013: Mesoscale Eddy Buoyancy Flux and Eddy-Induced Circulation in Eastern Boundary Currents. J. Phys. Oceanogr., 43, 1073–1095.
  14. Busecke, J., A. L. Gordon, Z. Li, F. M. Bingham, and J. Font, 2014: Subtropical surface layer salinity budget and the role of mesoscale turbulence, J. Geophys. Res. Oceans, 119, 4124–4140, doi:10.1002/2013JC009715
  15. Sepúlveda, H.H., P. Marchesiello , and Z. Li, 2013: Oceanic data assimilation study in northern Chile: use of a 3DVAR method, Latin American J. Aquatic Res., 41, 570-575.
  16. Li, Z., Y. Chao, J. C. Farrara, and J. C. McWilliams, 2012, Impacts of distinct observations during the 2009 Prince William Sound field experiment: A data assimilation study. Continental Shelf Research, DOI:10.1016/j.csr.2012.06.018.
  17. Li, Z., Z. Zang, Q. B. Li, Y. Chao, D. Chen, Z. Ye, Y. Liu, and K. N. Liou, 2013: A Three-Dimensional Variational Data Assimilation System for Multiple Aerosol Species with WRF/Chem and an Application to PM2.5 Prediction, Atmos. Chems. Phy., 13, 4265–4278, doi:10.5194/acp-13-4265-2013.
  18. Jin, X., C. Dong, J. Burian, J. C. McWilliams, D. B. Chelton, Z. Li, 2009: SST-Wind Interaction in Coastal Upwelling: Oceanic Simulation with Empirical Coupling, J. Phy. Oceanogr., 39, 2957-2970.
  19. Wang, X., Y. Chao, C. Dong, J. Farrara, Z. Li, and coauthors, 2009: Modeling Tides in Monterey Bay, California, Deep Sea Research, II 52, 169-191.
  20. Chao, Y., Z. Li, J. D. Farrara, and P. Huang, 2009: Blended sea surface temperatures from multiple satellites and in-situ observations for coastal oceans. J. Atmos. Oceanic Technol., 10.1175/2009JTECHO592.1
  21. Chao, Y., Z. Li, J. Farrara, J.C. McWilliams, and co-authors, 2008: A Real-time ocean forecasting system for the Monterey Bay, California. Deep Sea Research, doi:10.1016/j.dsr2.2008.08.011.
  22. Chao, Y., Z . Li, J.D. Farrara, M.A. Moline and O.M.E. Schofield, 2008: Synergistic applications of autonomous underwater vehicles and Regional Ocean Modeling System in coastal ocean forecasting. Limnol Oceanogr., 53, 2251-2263.
  23. Ramp, S.R, P. Lermusiaux, R. E. Davis, Y. Chao, D. Fratantoni, N.E. Leonard, J.D. Paduan, F. Chavez, I. Shulman, J. Marsden, W. Leslie, and Z. Li, 2009: The Autonomous Ocean Sensing Network (AOSN) predictive skill experiment in the Monterey Bay, Deep Sea Research, doi:10.1016/j.dsr2.2008.08.013.
  24. Li, Z., Y. Chao, J.C. McWilliams, and K. Ide, 2008: A three-dimensional variational data assimilation scheme for the Regional Ocean Modeling System. J. Atmos. Oceanic Tech., 25, 2074-2090.
  25. Li, Z., Y. Chao, J.C. McWilliams, and K. Ide, 2008: A three-dimensional variational data assimilation scheme for the Regional Ocean Modeling System: Implementation and basic experiments. J. Geophy. Res., doi:10.1029/2006JC004042.
  26. Li, Z., Y. Chao, and J.C. McWilliams, 2006: Computation of the streamfunction and velocity potential for limited and irregular domains. Mon. Weather. Rev., 134, 3384-3394.
  27. Li, Z., I. M. Navon, and Y.M. Hussaini, 2005: Analysis of the singular vectors of the full-physics FSU global spectral model. Tellus A, 57, 560-574.
  28. Chao, Y., Z. Li, J. Kindle, F. Chavez, and J. Paduan, 2003: A high-resolution surface vector wind product forcoastal oceans: Blending satellite scatterometer measurements with regional mesoscale atmospheric model simulations. Geophys. Res. Lett., 30, 1013, doi10.1029/2002GL015729.
  29. Li, Z., I.M. Navon, Y.M. Hussaini, and F.-X. Le Dimet, 2003: Optimal Control of Cylinder wakes via suction and blowing. The Computer & Fluid, 32, 149-171.
  30. Homescu C, Navon IM, Li Z, 2002: Suppression of vortex shedding for flow around a circular cylinder using optimal control. Internat. J. Numer. Methods Fluids, 38, 43-69.
  31. Li, Z., and I. M. Navon, 2001: Optimality of Variational Data Assimilation and Its Relationship with the Kalman Filter and Kalman Smoother. Quart. J. Roy. Meteorol. Soc., 127, 661-684.
  32. Li, Z., I. M. Navon, and Y. Zhu, 2000: Performance of 4D-Var Strategies Using the FSU Global Spectral Model with Its Full Physics Adjoint. Mon. Wea. Rev., 128, 668-688.
  33. Li, Z., A. Barcilon, and I. M. Navon, 1998: Study of Block Onset Using Sensitivity Perturbations in Climatological Flows. Mon. Wea. Rev., 127, 879-900.
  34. Li, Z., and I. M. Navon, 1998: Adjoint Sensitivity of the Earth's Radiation Budget in the NCEP/MRF Model. J. Geophy. Res., 103 (D4), 3801-3814.
  35. Li, Z., and L. Ji, 1997: Efficient Forcing and Teleconnection Patterns. Quart. J. Roy. Meteorol. Soc., 123, 2401-2423.

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